SODa: An Mn/Fe superoxide dismutase prediction and design server

Background: Superoxide dismutases (SODs) are ubiquitous metalloenzymes that play an important role in the defense of aerobic organisms against oxidative stress, by converting reactive oxygen species into nontoxic molecules. We focus here on the SOD family that uses Fe or Mn as cofactor. Results: The SODa webtool http://babylone.ulb.ac.be/soda predicts if a target sequence corresponds to an Fe/Mn SOD. If so, it predicts the metal ion specificity (Fe, Mn or cambialistic) and the oligomerization mode (dimer or tetramer) of the target. In addition, SODa proposes a list of residue substitutions likely to improve the predicted preferences for the metal cofactor and oligomerization mode. The method is based on residue fingerprints, consisting of residues conserved in SOD sequences or typical of SOD subgroups, and of interaction fingerprints, containing residue pairs that are in contact in SOD structures. Conclusion: SODa is shown to outperform and to be more discriminative than traditional techniques based on pairwise sequence alignments. Moreover, the fact that it proposes selected mutations makes it a valuable tool for rational protein design. © 2008 Kwasigroch et al; licensee BioMed Central Ltd.Journal ArticleResearch Support, Non-U.S. Gov'tinfo:eu-repo/semantics/publishe

PoPMuSiC 2.1: a web server for the estimation of protein stability changes upon mutation and sequence optimality

ABSTRACT:Journal ArticleSCOPUS: ar.jinfo:eu-repo/semantics/publishe

A global taxonomy of loops in globular proteins.

A bank of loops from three to eight amino acid residues long has been constituted. On the basis of statistical analysis of occurrences of conformations and residue, loops could be divided into two parts: the side residues directly bonded to the secondary structure flanking element, and the inner part. The conformations of the side residues are correlated to the nature of their neighboring flanks, while the inner residues adopt conformations uncorrelated from one residue to the next; thus they are unrelated to the flanks. Two zones in the Ramachandran plot are important: alpha L and beta P. In particular, the high occurrence of alpha L, mainly occupied by glycine residues, is necessary to induce flexibility and thus allow loops to comply with the geometrical constraints of the flanks. An algorithm of clustering has been used to aggregate loops of the same length within families of similar 3D structures. At each position in each cluster, sequence and conformational signatures have been deduced if the occurrence of a residue (or a conformation) is higher than an equiprobable distribution over all clusters. The result is that some positions favor particular amino acids and conformations, which are typical of a cluster although not unique. This is an indication of a relation between structure and sequence in loops. A taxonomy is proposed that classifies the various clusters. It relies on two terms: the mean distance between the first and last C alpha in one cluster and, perpendicular to this line, the distance to the center of gravity of the cluster. It is noteworthy that the differently populated clusters represented in such 2D plots can be separated. Thus, although the conformations of loops in globular proteins could cover a continuum, it has been possible to cluster them into a limited number of well populated families and superfamilies. This basic feature of protein architecture could be further exploited to better predict their geometry.info:eu-repo/semantics/publishe

SCooP: an accurate and fast predictor of protein stability curves as a function of temperature.

The molecular bases of protein stability remain far from elucidated even though substantial progress has been made through both computational and experimental investigations. One of the most challenging goals is the development of accurate prediction tools of the temperature dependence of the standard folding free energy ΔG(T). Such predictors have an enormous series of potential applications, which range from drug design in the biopharmaceutical sector to the optimization of enzyme activity for biofuel production. There is thus an important demand for novel, reliable and fast predictors.SCOPUS: ar.jinfo:eu-repo/semantics/publishe

BeAtMuSiC: prediction of changes in protein-protein binding affinity on mutations.

The ability of proteins to establish highly selective interactions with a variety of (macro)molecular partners is a crucial prerequisite to the realization of their biological functions. The availability of computational tools to evaluate the impact of mutations on protein-protein binding can therefore be valuable in a wide range of industrial and biomedical applications, and help rationalize the consequences of non-synonymous single-nucleotide polymorphisms. BeAtMuSiC (http://babylone.ulb.ac.be/beatmusic) is a coarse-grained predictor of the changes in binding free energy induced by point mutations. It relies on a set of statistical potentials derived from known protein structures, and combines the effect of the mutation on the strength of the interactions at the interface, and on the overall stability of the complex. The BeAtMuSiC server requires as input the structure of the protein-protein complex, and gives the possibility to assess rapidly all possible mutations in a protein chain or at the interface, with predictive performances that are in line with the best current methodologies.JOURNAL ARTICLESCOPUS: ar.jinfo:eu-repo/semantics/publishe

SOLart: a structure-based method to predict protein solubility and aggregation

Abstract Motivation The solubility of a protein is often decisive for its proper functioning. Lack of solubility is a major bottleneck in high-throughput structural genomic studies and in high-concentration protein production, and the formation of protein aggregates causes a wide variety of diseases. Since solubility measurements are time-consuming and expensive, there is a strong need for solubility prediction tools. Results We have recently introduced solubility-dependent distance potentials that are able to unravel the role of residue–residue interactions in promoting or decreasing protein solubility. Here, we extended their construction by defining solubility-dependent potentials based on backbone torsion angles and solvent accessibility, and integrated them, together with other structure- and sequence-based features, into a random forest model trained on a set of Escherichia coli proteins with experimental structures and solubility values. We thus obtained the SOLart protein solubility predictor, whose most informative features turned out to be folding free energy differences computed from our solubility-dependent statistical potentials. SOLart performances are very good, with a Pearson correlation coefficient between experimental and predicted solubility values of almost 0.7 both in cross-validation on the training dataset and in an independent set of Saccharomyces cerevisiae proteins. On test sets of modeled structures, only a limited drop in performance is observed. SOLart can thus be used with both high-resolution and low-resolution structures, and clearly outperforms state-of-art solubility predictors. It is available through a user-friendly webserver, which is easy to use by non-expert scientists. Availability and implementation The SOLart webserver is freely available at http://babylone.ulb.ac.be/SOLART/. Supplementary information Supplementary data are available at Bioinformatics online.info:eu-repo/semantics/publishe

New statistical potentials for probing protein binding affinity at the interactome scale

info:eu-repo/semantics/publishe

Quantification of biases in predictions of protein stability changes upon mutations

Motivation: Bioinformatics tools that predict protein stability changes upon point mutations have made a lot of progress in the last decades and have become accurate and fast enough to make computational mutagenesis experiments feasible, even on a proteome scale. Despite these achievements, they still suffer from important issues that must be solved to allow further improving their performances and utilizing them to deepen our insights into protein folding and stability mechanisms. One of these problems is their bias toward the learning datasets which, being dominated by destabilizing mutations, causes predictions to be better for destabilizing than for stabilizing mutations. Results: We thoroughly analyzed the biases in the prediction of folding free energy changes upon point mutations (Delta Delta G(0)) and proposed some unbiased solutions. We started by constructing a dataset S-sym of experimentally measured Delta Delta G(0)s with an equal number of stabilizing and destabilizing mutations, by collecting mutations for which the structure of both the wild-type and mutant protein is available. On this balanced dataset, we assessed the performances of 15 widely used Delta Delta G(0) predictors. After the astonishing observation that almost all these methods are strongly biased toward destabilizing mutations, especially those that use black-box machine learning, we proposed an elegant way to solve the bias issue by imposing physical symmetries under inverse mutations on the model structure, which we implemented in PoPMuSiC(sym). This new predictor constitutes an efficient trade-off between accuracy and absence of biases. Some final considerations and suggestions for further improvement of the predictors are discussed

PoPMuSiC, rationally designing point mutations in protein structures

PoPMuSiC is an efficient tool for rational computer-aided design of single-site mutations in proteins and peptides. Two types of queries can be submitted. The first option allows to estimate the changes in folding free energy for specific point mutations given by the user. In the second option, all possible point mutations in a given protein or protein region are performed and the most stabilizing or destabilizing mutations, or the neutral mutations with respect to thermodynamic stability, are selected. For each sequence position or secondary structure the deviation from the most stable sequence is moreover evaluated, which helps to identify the most suitable sites for the introduction of mutations.Journal Articleinfo:eu-repo/semantics/publishe

SpikePro: a webserver to predict the fitness of SARS-CoV-2 variants.

The SARS-CoV-2 virus has shown a remarkable ability to evolve and spread across the globe through successive waves of variants since the original Wuhan lineage. Despite all the efforts of the last 2 years, the early and accurate prediction of variant severity is still a challenging issue which needs to be addressed to help, for example, the decision of activating COVID-19 plans long before the peak of new waves. Upstream preparation would indeed make it possible to avoid the overflow of health systems and limit the most severe cases.info:eu-repo/semantics/publishe